Spatial dependence and heterogeneity in the location processes of new high‐tech firms in Nanjing, China

Li, Yingcheng; Zhu, Kai

doi:10.1111/pirs.12202

Cited by 17 publications

(17 citation statements)

References 42 publications

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“…In fact, the significant roles of development zones in promoting agglomeration of high-tech firms and industries has been confirmed in many empirical studies (Li & Zhu, 2017;Zhang, Huang, Sun, & Wang, 2013). In fact, the significant roles of development zones in promoting agglomeration of high-tech firms and industries has been confirmed in many empirical studies (Li & Zhu, 2017;Zhang, Huang, Sun, & Wang, 2013).…”

Section: Development Zones In China's Suburban Areasmentioning

confidence: 84%

“…As a major contributor to the suburbanization processes of Chinese cities, development zones are not only containers of manufacturing firms and related service companies but can also be incubators of new inventions and knowledge. In fact, the significant roles of development zones in promoting agglomeration of high-tech firms and industries has been confirmed in many empirical studies (Li & Zhu, 2017;Zhang, Huang, Sun, & Wang, 2013). In addition to the fact that development zones are usually located in the suburbs, there are at least two other factors that can facilitate the processes of knowledge generation and transfer in development zones.…”

Section: Development Zones In China's Suburban Areasmentioning

confidence: 92%

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Innovation in suburban development zones: Evidence from Nanjing, China

Wang

2018

Growth and Change

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While suburban areas have been typically related with urban sprawl, low density, cheap land price and low‐end manufacturing industries, relatively little attention has been paid to the spatial organization of innovation in suburban areas. In some Chinese cities, development zones (kai fa qu) in their suburban areas which are generally regarded as major spaces for traditional manufacturing industries have been transforming into spaces for innovation. This paper provides a case study of Nanjing, the capital of Jiangsu province in Eastern China by investigating and explaining the learning and innovation processes in suburban development zones. Drawing upon a unique database on 733 state‐certified high‐tech firms in Nanjing from 2008 to 2012 which contains detailed information on their addresses and number of patents, we observe strong spatial disparity in the distribution of innovation. Suburban districts of Nanjing where its three national‐level development zones and many provincial‐level development zones are located have become major spaces for innovation of the city. The planning of industrial clusters in development zones, the place‐based investment in the suburbs, and the suburbanization of universities are three major mechanisms behind the learning and innovation processes in Nanjing’s suburban development zones.

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Section: Development Zones In China's Suburban Areasmentioning

confidence: 84%

Section: Development Zones In China's Suburban Areasmentioning

confidence: 92%

Innovation in suburban development zones: Evidence from Nanjing, China

Wang

2018

Growth and Change

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“…A study by Shima showed that regional compactness has a significant positive impact on innovative firms and regional innovation capacity [42,43]. Li found that accessibility can significantly affect the location choice for high-tech firms [29]. The research by Bettencourt demonstrated a super-linear relationship between the number of patent applications and the city size [44].…”

Section: Introductionmentioning

confidence: 99%

“…However, in studies on the location choice and the agglomeration of innovation, built environment was often conceptualized as part of the location factors. For example, the overall impact factor framework included the infrastructure [22], accessibility [29], and technology support levels [20]. Such an approach fails to provide a systematic analysis of the built environment.…”

Section: Introductionmentioning

confidence: 99%

Relationship Between the Built Environment and the Location Choice of High-Tech Firms: Evidence from the Pearl River Delta

Wang

et al. 2019

Sustainability

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With the transition in the regional development discourse to knowledge- and innovation-based economics, the cultivation of innovation capacity has gained importance as an initiative to enhance regional sustainability and has emerged as a policy goal. An understanding of the formation of innovation clusters is critical to the cultivation of regional innovation capabilities. Except for the location and regional development conditions’ factors, researchers emphasize a built environment’s role in the formation of innovation clusters. Based on the spatial database of 12,516 high-tech firms in 2017 in the Pearl River Delta (PRD), this study developed a conceptual framework for the built environment. The framework comprises living, service, perceptual, industrial, and physical aspects. The direction and intensity of the correlation between built environment factors and high-tech firms are discussed using the spatial regression model and geographical detector (GD) technique. The results show a highly concentrated spatial distribution pattern of high-tech firms in the PRD. A significant county-level spatial autocorrelation is revealed through Moran’s I. According to the model, we determine the positive impacts of technology support, transport infrastructure, and living service levels on the agglomeration of high-tech firms as well as the negative impact of the public service level. The GD’s result demonstrates different levels of impact intensity of built environmental factors. We argue that a comprehensive understanding of the influence of built environment factors on innovation agglomeration will help policymakers develop targeted policies to foster local innovation capabilities and promote sustainable regional development.

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“…Taking the primary existing literature into account, Autant‐Bernard () investigated the location of research and development (R&D) activities in France; Arauzo‐Carod and Manjón‐Antolín () and Alamá‐Sabater et al . () of manufacturing establishments and firm birth, respectively, in Spain; and Li and Zhu () of R&D in Nanjing, China. In Japan, as far as we know, only Nguyen et al .…”

Section: Introductionmentioning

confidence: 99%

Spatial Modeling of Industrial Location Determinants in Japan: Empirical Analysis Using Spatial Econometric Approaches

Takano

Tsutsumi

Kikukawa

2018

Rev Urban & Reg Develop

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Recent empirical studies have shown that investigation is required to identify the determinants of industrial location on a spatially detailed scale for disaggregated regional units, such as cities or counties. The existing literature establishes the necessity for considering spatial dependence among proximal location units; however, few studies have employed this process for Japan. We empirically show the main determinants of new entries of production bases in 1,652 local municipalities in Japan from 2012 to 2015. We also address the existence of spatial dependence by adopting a spatial econometric approach, which has an impact on the location determination process. Some location determinants of neighboring municipalities, related to economic agglomeration, have a significant impact on the new entries of production bases belonging to the heavy industry, while no significant impact is observed in the light industry. Unobserved and spatially dependent determinants have a significantly positive impact on new production bases in both industries, particularly in disaster-stricken areas.

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Spatial dependence and heterogeneity in the location processes of new high‐tech firms in Nanjing, China

Cited by 17 publications

References 42 publications

Innovation in suburban development zones: Evidence from Nanjing, China

Innovation in suburban development zones: Evidence from Nanjing, China

Relationship Between the Built Environment and the Location Choice of High-Tech Firms: Evidence from the Pearl River Delta

Spatial Modeling of Industrial Location Determinants in Japan: Empirical Analysis Using Spatial Econometric Approaches

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